Load libraries
Loading required package: Matrix
Loading required package: igraph
package ‘igraph’ was built under R version 3.5.3
Attaching package: ‘igraph’
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
Attaching package: ‘pagoda2’
The following objects are masked _by_ ‘.GlobalEnv’:
armaCor, gene.vs.molecule.cell.filter, namedNames, read.10x.matrices, show.app
Attaching package: ‘dplyr’
The following objects are masked from ‘package:igraph’:
as_data_frame, groups, union
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Attaching package: ‘conos’
The following objects are masked _by_ ‘.GlobalEnv’:
plotClusterBarplots, plotComponentVariance, plotDEheatmap
Loading required package: ggplot2
Attaching package: ‘ggplot2’
The following object is masked _by_ ‘.GlobalEnv’:
qplot
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggplot2’:
ggsave
Load kidney data
source("~/m/pagoda2/R/helpers.r")
path <- "/d0-mendel/home/pkharchenko/ninib/NB/kidney/GSE114530"
pl <- paste(path,list.files(path=path),sep='/')
names(pl) <- list.files(path=path)
pl <- pl[!grepl('w9',names(pl))]
min.size <- 1e3;
cdl <- read.10x.matrices(pl,n.cores=30)
cdl <- lapply(cdl,function(d) d[,colSums(d)>=min.size,drop=F])
mdl <- lapply(pl,function(d) read.delim(paste(d,'annot.csv',sep='/'),sep=',',header=T,stringsAsFactors = F))
kidney.cell.f <- setNames(unlist(lapply(mdl,function(d) d[,2])),unlist(lapply(pagoda2:::sn(names(mdl)),function(n) paste(n,mdl[[n]][,1],sep='_'))))
Get cell cycle genes
cc.genes <- readRDS("~pkharchenko/m/ninib/NB/cc.genes.rds")
Generate p2 apps, omitting cell cycle genes and re-calculating variance norm
cdl.p2 <- lapply(cdl,function(cd) {
cd <- cd[!rownames(cd)%in%cc.genes[,2], ]
basicP2proc(cd,n.cores=30,make.geneknn = FALSE,get.tsne = T,get.largevis = FALSE)
})
5755 cells, 32871 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 689 overdispersed genes ... 689persisting ... done.
running PCA using 3000 OD genes .... done
creating space of type angular done
adding data ... done
building index ... done
querying ... done
running tSNE using 30 cores:
7114 cells, 32871 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ...
#cdl.p2 <- lapply(cdl,basicP2proc,n.cores=20,min.cells.per.gene=-1, get.largevis=F, get.tsne=T, make.geneknn=F,n.odgenes=2e3, nPcs=50)
Load adrenal data This contains adrenal data
ncdconA <- readRDS("~pkharchenko/m/ninib/NB/ncdconA.rds")
neww_annot <- readRDS("~pkharchenko/m/ninib/NB/neww_annot.rds")
ap2 <- readRDS("~pkharchenko/m/ninib/NB/figures/fig1/ap2.p2.rds")
Recalculate without cell cycle genes and cluster 10
x <- ap2$misc$rawCounts
f <- ap2$clusters$PCA[[1]];
x <- x[!rownames(x) %in% names(f)[f==10], !colnames(x) %in% cc.genes[,2]]
ap2b <- basicP2proc(t(x),n.cores=30,make.geneknn = FALSE,get.tsne = T,get.largevis = FALSE)
7706 cells, 32696 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 2220 overdispersed genes ... 2220persisting ... done.
running PCA using 3000 OD genes .... done
creating space of type angular done
adding data ... done
building index ... done
querying ... done
running tSNE using 30 cores:
cona <- Conos$new(c(cdl.p2,list("Adr"=ap2b)))
cona$buildGraph(k=15,k.self=5,k.self.weigh=0.1,ncomps=30,n.odgenes=2e3,space='PCA')
found 0 out of 10 cached PCA space pairs ... running 10 additional PCA space pairs done
inter-sample links using mNN done
local pairs local pairs done
building graph ..done
cona$findCommunities(method=leiden.community,resolution=1)
cona$embedGraph(method='UMAP',seed=0)
Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 08:23:25.
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Done.
Estimating commute distances: 08:23:29.
Hashing adjacency list: 08:23:29.
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[----|----|----|----|----|----|----|----|----|----|
****************************************
Done.
**********|
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[----|----|----|----|----|----|----|----|----|----|
Estimating distances: 08:23:30.
**************************************************|
Done
Done.
All done!: 08:23:33.
Done
Estimate UMAP embedding...
Maximal number of estimated neighbors is 37. Consider increasing min.visited.verts, min.prob or min.prob.lower.
08:23:33 UMAP embedding parameters a = 0.0267 b = 0.7906
08:23:33 Read 34141 rows and found 1 numeric columns
08:23:35 Commencing smooth kNN distance calibration using 64 threads
08:23:39 Initializing from normalized Laplacian + noise
08:23:42 Commencing optimization for 1000 epochs, with 869504 positive edges using 64 threads
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:24:03 Optimization finished
Done
cona$misc$embeddings <- list("UMAP"=cona$embedding)
size <- 0.1; alpha <- 0.5;
tissuef <- cona$getDatasetPerCell();
tissuef <- as.factor(setNames(ifelse(grepl('^w',names(tissuef)),'kidney','adrenal'),names(tissuef)))
pl <- list(cona$plotGraph(groups=tissuef, size=size, alpha=alpha,title='tissue',mark.groups=F),
cona$plotGraph(groups=ap2$clusters$PCA[[1]],size=size, alpha=alpha,title='adrenal clusters',plot.na=F),
cona$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,title='kidney',font.size=c(3,5),plot.na=F),
cona$plotGraph(groups = neww_annot, size=size, alpha=alpha,title='adrenal',plot.na=F))
plot_grid(plotlist=pl,nrow=2)

cona$plotGraph(groups=setNames(ap2$clusters$PCA[[1]]==7,names(ap2$clusters$PCA[[1]])),size=size, alpha=alpha,title='adrenal clusters',plot.na=F)

annot <- readRDS("/d0-mendel/home/meisl/Workplace/neuroblastoma/cell.annotation.Jan2020.rds")$cellano
annot <- setNames(as.character(annot),names(annot))
annot[annot=='unknown'] <- 'Erythroid'
annot[annot=='Plasmacytoid'] <- 'pDC'
annot[grep('Cytotoxic',annot)] <- 'Tcyto'
annot[grep('T helper',annot)] <- 'Th'
annot[grep('NK',annot)] <- 'NK'
annot[grep('Bcell',annot)] <- 'B'
annot[grep('PlasmaCell',annot)] <- 'Plasma'
annot[grep('Mast',annot)] <- 'Mast'
annot[grep("Bridge",annot)] <- 'SCP-like'
dannot <- as.factor(annot)
annot[grep("SOX11|Stress|euronal|Prolif",annot)] <- 'Noradrenergic'
annot[grep("Bridge",annot)] <- 'SCP-like'
annot <- as.factor(annot);
# renamings
levels(annot) <- gsub("Noradrenergic","Adrenergic",levels(annot))
Load annotations and palettes:
load("../figures/fig1/annotations.RData")
Align everything together:
conb <- Conos$new(c(cdl.p2,ncdconA$samples[grepl("^NB",names(ncdconA$samples))],list("Adr"=ap2b)))
conb$buildGraph(k=15,k.self=20,k.self.weigh=0.5,ncomps=30,n.odgenes=2e3,space='PCA')
found 231 out of 231 cached PCA space pairs ... done
inter-sample links using mNN done
local pairs local pairs done
building graph ..done
conb$findCommunities(method=leiden.community,resolution=1)
conb$embedGraph(method='UMAP',seed=0, min.dist=0.01, spread=10, min.prob.lower=1e-3,n.epochs=2e3)
Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 11:06:27.
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Done.
Estimating commute distances: 11:06:34.
Hashing adjacency list: 11:06:34.
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[----|----|----|----|----|----|----|----|----|----|
***************************************
Done.
***********|
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[----|----|----|----|----|----|----|----|----|----|
Estimating distances: 11:06:37.
**************************************************|
Done
Done.
All done!: 11:07:00.
Done
Estimate UMAP embedding...
11:07:01 UMAP embedding parameters a = 0.05039 b = 0.7915
11:07:01 Read 107335 rows and found 1 numeric columns
11:07:05 Commencing smooth kNN distance calibration using 64 threads
11:07:13 Initializing from normalized Laplacian + noise
11:07:20 Commencing optimization for 2000 epochs, with 4186840 positive edges using 64 threads
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:08:33 Optimization finished
Done
conb$misc$embeddings <- list("UMAP"=conb$embedding)
conb$misc$embeddings$largeVis <- conb$embedGraph(method='largeVis',alpha=3, sgd_batches = 3e8)
Estimating embeddings.
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
conb$misc$embeddings$UMAP2 <- conb$embedGraph(method='UMAP', min.dist=0.01, spread=15, min.prob.lower=1e-3,n.epochs=2e3)
Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 11:16:25.
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Done.
Estimating commute distances: 11:16:32.
Hashing adjacency list: 11:16:32.
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
******************************
Done.
********************|
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
Estimating distances: 11:16:34.
**************************************************|
Done
Done.
All done!: 11:16:57.
Done
Estimate UMAP embedding...
11:16:58 UMAP embedding parameters a = 0.02659 b = 0.7912
11:16:58 Read 107335 rows and found 1 numeric columns
11:17:02 Commencing smooth kNN distance calibration using 64 threads
11:17:09 Initializing from normalized Laplacian + noise
11:17:17 Commencing optimization for 2000 epochs, with 4186840 positive edges using 64 threads
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
11:18:27 Optimization finished
Done
size <- 0.1; alpha <- 0.1;
tissuef <- conb$getDatasetPerCell();
tissuef <- setNames(as.character(tissuef),names(tissuef))
tissuef[grep("^w",tissuef)] <- 'kidney'
tissuef[grep("^Adr",tissuef)] <- 'adrenal'
tissuef[grep("^NB",tissuef)] <- 'NB'
pl <- list(conb$plotGraph(groups=tissuef, size=size, alpha=alpha,title='tissue',mark.groups=F),
conb$plotGraph(groups=annot,size=size, alpha=alpha,title='neuroblastoma',plot.na=F),
conb$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,title='kidney',font.size=c(3,5),plot.na=F),
#conb$plotGraph(groups = neww_annot, size=size, alpha=alpha,title='adrenal',plot.na=F))
conb$plotGraph(groups = ap2$clusters$PCA[[1]], size=size*4, alpha=alpha,title='adrenal',plot.na=F))
plot_grid(plotlist=pl,nrow=2)

ninib_bannot <- readRDS("~pkharchenko/m/ninib/NB/ninib_bannot.rds")
Update adrenal annotation
adr.ann <- setNames(as.character(neww_annot),names(neww_annot));
adr.ann[adr.ann=='Immune cells'] <- 'Kidney cells'
adr.ann[adr.ann=='Vessels'] <- 'Endothelial'
adr.ann[adr.ann=='Blood cells'] <- 'Erythroid'
adr.ann[adr.ann=='Pericytes'] <- 'Mesenchymal'
adr.ann[adr.ann=='Sympatoblasts'] <- 'Sympathoblasts'
adr.ann[adr.ann=='Schwann cells'] <- 'SCPs'
cl <- ap2$clusters$PCA[[1]]
adr.ann[names(adr.ann) %in% names(cl)[cl=='18']] <- "Myeloid"
adr.ann[names(adr.ann) %in% names(cl)[cl=='11']] <- "Lymphoid"
adr.ann[names(adr.ann) %in% names(cl)[cl=='14']] <- "Kidney cells"
adr.ann <- as.factor(adr.ann)
set.seed(4)
adr.pal <- sample(rainbow(length(levels(annot)),v=0.95,s=0.85));
adr.palf <- function(n) return(adr.pal)
With density contour
linetype <- 1; color='grey20';
kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(annot)[annot=='Noradrenergic'],], compute.cont=TRUE)
cn <- with(kd, contourLines(x=eval.points[[1]], y=eval.points[[2]],z=estimate, levels=cont["10%"])[[1]])
cn <- geom_path(aes(x, y), data=data.frame(cn),linetype = linetype , color=color);
kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(annot)[annot=='Mesenchymal'],], compute.cont=TRUE)
cm <- with(kd, contourLines(x=eval.points[[1]], y=eval.points[[2]],z=estimate, levels=cont["10%"])[[1]])
cm <- geom_path(aes(x, y), data=data.frame(cm),linetype = linetype , color=color);
kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(annot)[annot=='SCP-like'],], compute.cont=TRUE, approx.cont = F)
#kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(ninib_bannot)[ninib_bannot=='Fibroblast_Bridge'],], compute.cont=TRUE)
cb <- with(kd, contourLines(x=eval.points[[1]], y=eval.points[[2]],z=estimate, levels=cont["50%"])[[1]])
cb <- geom_path(aes(x, y), data=data.frame(cb),linetype = linetype , color=color);
size <- 0.1; alpha <- 0.1;
tissuef <- conb$getDatasetPerCell();
tissuef <- setNames(as.character(tissuef),names(tissuef))
tissuef[grep("^w",tissuef)] <- 'kidney'
tissuef[grep("^Adr",tissuef)] <- 'adrenal'
tissuef[grep("^NB",tissuef)] <- 'NB'
raster <- T;
#af <- setNames(names(tissuef) %in% names(ninib_bannot)[ninib_bannot=='Fibroblast_Bridge'],names(tissuef))
pl <- list(conb$plotGraph(groups=tissuef, size=size, alpha=alpha,raster=raster, title='tissue',mark.groups=F)+ theme(legend.position=c(0.85, 0.15)) + guides(color=guide_legend(override.aes = list(size=3,alpha=0.8),title='Tissue:')),
conb$plotGraph(groups=annot,size=size, alpha=alpha,raster=raster,title='neuroblastoma',plot.na=F,palette=annot.pal),
conb$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,raster=raster,title='kidney',font.size=c(3,5),plot.na=F),
conb$plotGraph(groups = adr.ann, size=size*4, alpha=alpha*2,raster=raster,title='adrenal',plot.na=F,palette=adr.palf,font.size=c(4,5)))
#conb$plotGraph(groups = ap2$clusters$PCA[[1]], size=size*4, alpha=alpha*2,title='adrenal',plot.na=F))
pl <- lapply(pl,function(p) p+cn+cm+cb+ xlim(range(conb$embedding[,1]))+ylim(range(conb$embedding[,2])))
pp <- plot_grid(plotlist=pl,nrow=2)
pp

pdf('combined.pdf',width=10,heigh=10); print(pp); dev.off();
null device
1
invisible(lapply(1:length(pl),function(i) { pdf(paste('panel',i,'pdf',sep='.'),width=4,height=4); print(pl[[i]]); dev.off()}))
Restricted window plot for the main figure:
size <- 0.3; alpha <- 0.1;
raster <- T;
xlim <- c(-66,40); ylim <- c(-63,0)
#af <- setNames(names(tissuef) %in% names(ninib_bannot)[ninib_bannot=='Fibroblast_Bridge'],names(tissuef))
pl <- list(conb$plotGraph(groups=tissuef, size=size, alpha=alpha,raster=raster, raster.width=6.6,raster.height=4, title='tissue',mark.groups=F),
conb$plotGraph(groups=annot,size=size, alpha=alpha,raster=raster,title='neuroblastoma', raster.width=6.6,raster.height=4,plot.na=F,palette=annot.pal),
#conb$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,raster=raster,title='kidney',font.size=c(3,5),plot.na=F),
conb$plotGraph(groups = adr.ann, size=size*4, alpha=alpha*2,raster=raster,title='adrenal', raster.width=6.6,raster.height=4,plot.na=F,palette=adr.palf,font.size=c(4,5)))
#conb$plotGraph(groups = ap2$clusters$PCA[[1]], size=size*4, alpha=alpha*2,title='adrenal',plot.na=F))
#pl <- lapply(pl,function(p) p+cn+cm+cb+ coord_cartesian(xlim,ylim,clip="on"))
# pl <- lapply(pl,function(p) p+cn+cm+cb+scale_x_continuous(expand = c(0, 0)) +
# scale_y_continuous(expand = c(0, 0)) + xlim(xlim)+ylim(ylim)
# )
pl <- lapply(pl,function(p) p+cn+cm+cb+coord_cartesian(expand = c(0, 0), xlim=xlim,ylim=ylim) +xlim(xlim)+ylim(ylim))
pp <- plot_grid(plotlist=pl,ncol=1)
the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedRemoved 68617 rows containing missing values (geom_point_rast).the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedRemoved 49920 rows containing missing values (geom_point_rast).Removed 13 rows containing missing values (geom_label_repel).the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedRemoved 5128 rows containing missing values (geom_point_rast).Removed 5 rows containing missing values (geom_label_repel).
pdf('combined_narrow.pdf',width=3.5,heigh=6); print(pp); dev.off();
png
2
pp

conb$plotGraph(gene='NR5A1',alpha=0.4,size=0.1)

jgrp <- conb$findCommunities(method=leiden.community,r=4)$groups
conb$plotGraph(alpha=0.1,size=0.1,mark.groups=T,groups=jgrp)

Illustrate similarity between bridge and SCPs:
Identify genes that set both populations apart from others:
fac <- conb$getDatasetPerCell()
fac <- setNames(rep('other',length(fac)),names(fac))
#fac[names(fac) %in% c(names(adr.ann)[adr.ann=='Schwann cells'], names(annot)[annot=='SCP-like'])] <- 'SCPs'
schwann.de <- conb$getDifferentialGenes(groups=fac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
source("~/m/p2/conos/R/plot.R")
sfac <- fac; sfac <- sfac[unlist(tapply(1:length(fac),fac,sample,min(table(fac))))]
pp <- plotDEheatmap(conb,sfac,schwann.de,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 6,column.metadata = list(tissue=tissuef))
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
fac2 <- conb$getDatasetPerCell()
fac2 <- setNames(rep('other',length(fac2)),names(fac2))
#fac[names(fac) %in% c(names(adr.ann)[adr.ann=='Schwann cells'], names(annot)[annot=='SCP-like'])] <- 'SCPs'
fac2[names(fac2) %in% c(names(jgrp)[jgrp=='37'])] <- 'SCPs'
schwann.de2 <- conb$getDifferentialGenes(groups=fac2,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
source("~/m/p2/conos/R/plot.R")
sfac <- as.factor(fac2); sfac <- droplevels(sfac[unlist(tapply(1:length(fac),fac,sample,min(table(fac))))])
pp <- plotDEheatmap(conb,sfac,schwann.de2,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 7,column.metadata = list(tissue=tissuef))
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp

There’s a group within the ‘other’ that seems to share the SCP pattern …
conb$plotGraph(gene='PLP1',alpha=0.4,size=0.1)

conb$plotGraph(gene='S100B',alpha=0.4,size=0.1)

qplot <- function(g, con.obj, ann) {
#cat(g,' ')
x <- lapply(con.obj$samples[5:10],function(r) { if(g%in% colnames(r$counts)) { r$counts[,g] } else { return(NULL) } })
if(length(unlist(x))<1) stop('gene ',g,' is not found')
df <- data.frame(val=unlist(x),cell=unlist(lapply(x,names)))
df$cluster <- ann[match(df$cell,names(ann))]
df <- na.omit(df)
mv <- max(tapply(df$val,df$cluster,quantile,p=0.8),tapply(df$val,df$cluster,mean))*1.5
p <- ggplot(df,aes(x=cluster,y=val,color=cluster))+geom_boxplot(outlier.shape = NA)+ stat_summary(fun.data=mean_se,geom="pointrange", color="black")+ylab(g)+ggtitle(g)+guides(colour=FALSE)+ theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.5))+coord_cartesian(ylim=c(0,mv));
p
}
markers <- c(as.character(schwann.de2$SCPs %>% arrange(-AUC) %>% head(8) %>% '$'('Gene')),'SOX10','MIA')
pl <- lapply(markers,qplot,conb,jgrp)
plot_grid(plotlist=pl,ncol=2)

It’s cluster 35, which is the extension of the bridge into the fibroblasts … most likely as a result of clustering uncertainty. The cluster should be better defined on NB alone …
Check expression on the NB embedding
ncdcon <- readRDS("~pkharchenko/m/ninib/NB/ncdcon.rds")
p1 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=ncdcon$clusters$rleiden$groups,plot.na=F,mark.groups=T)
p2 <- ncdcon$plotGraph(alpha=0.03,size=0.5,plot.na=F,mark.groups=T,groups=fac)
p3 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=fac2,plot.na=F,mark.groups=T)
plot_grid(plotlist=list(p1,p2,p3),nrow=1)

pl <- lapply(markers,qplot,ncdcon,ncdcon$clusters$rleiden$groups)
plot_grid(plotlist=pl,ncol=2)

Here to, while cluster 1-5 captures most of the SCPs, part of the signature is still notable in the cluster 5-1 that connects to the messenchymal tumor cells
Also, in doing DE on fac2, the adrenal and NB contributions to the SCP population are very unbalanced. It’s probably best to run the two separately and intersect the high-AUC genes:
fac3.adr <- conb$getDatasetPerCell()
fac3.adr <- setNames(rep('other',length(fac3.adr)),names(fac3.adr))
# remove NB SCP-like cells
fac3.adr <- fac3.adr[!names(fac3.adr) %in% c(names(jgrp)[jgrp=='35'], names(annot)[annot=='SCP-like'], intersect(names(annot),names(jgrp)[jgrp=='37']))]
fac3.adr[names(fac3.adr) %in% c(names(adr.ann)[adr.ann=='Schwann cells'])] <- 'adrenal SCP'
#fac3.adr[names(fac3.adr) %in% c(names(jgrp)[jgrp=='37'])] <- 'SCPs' # use a more restrictive cluster-based definition
fac3.adr <- droplevels(as.factor(fac3.adr))
table(fac3.adr,conb$getDatasetPerCell()[names(fac3.adr)])
fac3.adr Adr NB01 NB02 NB09 NB11 NB12 NB13 NB15 NB16 NB17 NB18 NB19 NB20 NB21 NB22 NB23 NB24 NB26 w11 w13 w16 w18
adrenal SCP 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
other 7646 979 1373 6529 10324 5149 17274 5429 2814 1553 4655 3242 534 480 1116 2586 2956 5130 5552 7072 8071 4866
And NB only cells
# factor for just the adrenal SCP cells
fac3.nb <- conb$getDatasetPerCell()
fac3.nb <- setNames(rep('other',length(fac3.nb)),names(fac3.nb))
# remove NB SCP-like cells
fac3.nb <- fac3.nb[!names(fac3.nb) %in% c(names(jgrp)[jgrp=='35'], names(adr.ann)[adr.ann=='Schwann cells'])]
fac3.nb[names(fac3.nb) %in% c(intersect(names(annot),names(jgrp)[jgrp=='37']))] <- 'SCP-like NB'
fac3.nb <- droplevels(as.factor(fac3.nb))
table(fac3.nb,conb$getDatasetPerCell()[names(fac3.nb)])
schwann.de3.nb <- conb$getDifferentialGenes(groups=fac3.nb,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
Join tables and separate common and distinct markers
require(magrittr)
df <- full_join(schwann.de3.adr[['adrenal SCP']], schwann.de3.nb[['SCP-like NB']], by='Gene',suffix=c('.adr','.nb'))
Column `Gene` joining factors with different levels, coercing to character vector
df$minAUC <- pmin(df$AUC.adr,df$AUC.nb)
df$maxAUC <- pmax(df$AUC.adr,df$AUC.nb,na.rm=T)
df %<>% arrange(-minAUC,maxAUC) %>% select(Gene,minAUC,maxAUC,AUC.nb,AUC.adr)
head(df,100)
tail(df,100)
Show combined expression pattern
genes.nb <- tail(df[is.na(df$AUC.adr),],7)$Gene #7
genes.adr <- tail(df[is.na(df$AUC.nb),],6)$Gene
genes.adr <- unique(c(genes.adr,'ASCL1'))
#genes.common <- c(head(df,14)$Gene,'SOX10','ASCL1','EGR2','DHH','OCT4','ISL1') #14
genes.common <- unique(c(head(df,13)$Gene,'SOX10',"FOXD3" , "PMP2", "LGI4", "SEMA3B")) #14
genes <- unique(c(genes.common,genes.adr,genes.nb))
#cell.factor <- as.factor(c(setNames(rep('NB SCP-like',length(c1)),c1),setNames(rep("adrenal SCP",length(c2)),c2)))
cell.factor <- c(setNames(as.character(fac3.nb),names(fac3.nb)),setNames(as.character(fac3.adr),names(fac3.adr)))
cell.factor <- cell.factor[unique(names(cell.factor))]
oc <- names(cell.factor)[cell.factor=='other']; cell.factor[oc] <- paste('other',as.character(tissuef[oc]),sep=' ')
cell.factor <- factor(cell.factor,levels=unique(cell.factor)[c(5,3,2,4,1)])
# omit kidney
cell.factor[cell.factor=='other kidney'] <- NA; cell.factor <- droplevels(na.omit(cell.factor))
xa <- do.call(rbind,lapply(pagoda2:::sn(genes),function(gene) conos:::getGeneExpression(conb,gene)))
Because the numbers of cells are so unbalanced, we’ll randomly collapse larger groups to approximately match the number of cells in the smaller groups
xc <- do.call(cbind, tapply(1:ncol(xa),cell.factor[colnames(xa)],function(ii) {
bin.size <- ceiling(length(ii)/min(table(cell.factor)))
nbins <- ceiling(length(ii)/bin.size)
if(bin.size >1 ) {
bf <- sample(rep(1:nbins,bin.size))[1:length(ii)]
#x <- do.call(cbind,tapply(1:length(ii),as.factor(bf),function(x) rowMeans(xa[,ii[x]])))
x <- do.call(cbind,tapply(1:length(ii),as.factor(bf),function(x) xa[,sample(ii[x],1),drop=F]))
#colnames(x) <- colnames(xa)[ii][1:ncol(x)]
x
} else {
return(xa[,ii])
}
}))
# trim
expression.quantile <- 0.75
x <- na.omit(xc)
#x <- na.omit(x[,names(cell.factor)])
x <- t(apply(x, 1, function(xp) {
if(expression.quantile<1) {
qs <- quantile(xp,c(1-expression.quantile,expression.quantile))
if(diff(qs)==0) { # too much, set to adjacent values
xps <- unique(xp)
if(length(xps)<3) { qs <- range(xp) } # only two values, just take the extremes
xpm <- median(xp)
if(sum(xp<xpm) > sum(xp>xpm)) { # more common to have values below the median
qs[1] <- max(xp[xp<xpm])
} else { # more common to have values above the median
qs[2] <- min(xps[xps>xpm]) # take the next one higher
}
}
xp[xp<qs[1]] <- qs[1]
xp[xp>qs[2]] <- qs[2]
}
xp <- xp-min(xp);
if(max(xp)>0) xp <- xp/max(xp);
xp
# qs <- quantile(xp,c(1-expression.quantile,expression.quantile))
# #xp[xp<qs[1]] <- qs[1]
# if(qs[2]>0) {
# xp[xp>qs[2]] <- qs[2]
# }
# xp-min(xp);
# xpr <- diff(range(xp));
# if(xpr>0) xp <- xp/xpr;
# xp
}))
require(ComplexHeatmap)
# column annotation
an <- data.frame(cells=cell.factor[colnames(x)],row.names = colnames(x))
# row annotation
gpal <- c("common SCP genes"='grey30','adrenal'='green','NB'='orange');
rdf <- data.frame(genes=rep(names(gpal),c(length(genes.common),length(genes.adr),length(genes.nb))),name=c(genes.common,genes.adr,genes.nb));
rdf <- rdf[!duplicated(rdf$name),];
rdf <- data.frame(genes=rdf$genes,row.names=rdf$name)
#rownames(rdf) <- c(genes.common,genes.adr,genes.nb);
rdf <- rdf[rownames(x),,drop=F]
ra <- ComplexHeatmap::HeatmapAnnotation(df=rdf,which='row',show_annotation_name=FALSE, show_legend=FALSE, border=T,col=list(genes=gpal))
ra <- ComplexHeatmap::HeatmapAnnotation(genes=anno_block(gp=gpar(fill=NA,col=NA),labels=names(gpal[levels(rdf$genes)]),labels_gp = gpar(fontsize=16) ),which='row',show_annotation_name=FALSE, show_legend=FALSE, border=T)
#ta <- ComplexHeatmap::HeatmapAnnotation(df=an,border=T,show_legend = T,col=list(cells=c('adrenal SCP'='darkorchid4','SCP-like NB'='red','other NB'='orange','other adrenal'='green','other kidney'='royalblue3')))
gpal <- c('adrenal SCP'='darkorchid4','SCP-like NB'='red','other NB'='orange','other adrenal'='green');
ta <- ComplexHeatmap::HeatmapAnnotation(cells=anno_block(gp=gpar(fill=NA,col=NA), labels=names(gpal), labels_rot = 35, labels_gp = gpar(just=-1, fontsize=14)))
#ComplexHeatmap::Heatmap(x,name='Expression',col=colorRampPalette(c('dodgerblue1','grey95','indianred1'))(1024), left_annotation=ra, cluster_rows = F, cluster_columns = FALSE,show_row_names = T, show_column_names = F,top_annotation = ta,border=T)
set.seed(0)
hm <- ComplexHeatmap::Heatmap(x,name='Expression',col=colorRampPalette(c('dodgerblue1','grey95','indianred1'))(1024), left_annotation=ra, cluster_rows = F, cluster_columns = FALSE,show_row_names = T, show_column_names = F,top_annotation = ta,border=T,column_split=an[colnames(x),1], row_split=rdf[,1], row_gap = unit(1, "mm"), column_gap = unit(1, "mm"),row_names_gp = grid::gpar(fontsize = 8),column_title = NULL, row_title = NULL, heatmap_legend_param = list(legend_direction='horizontal'),use_raster = T,raster_device = "CairoPNG")
hmp <- draw(hm, heatmap_legend_side = "bottom")

pdf(file='scp.markers2.pdf',width=4,height=7); print(hmp); dev.off();
png
2
Cell type similarity matrix
Let’s go with the simplest thing
source("~pkharchenko/m/pavan/DLI/conp2.r")
f1 <- setNames(as.character(adr.ann),names(adr.ann))
#f1[f1 %in% c("Monocytes","Mast",'mDC')] <- 'Myeloid'
f2 <- setNames(as.character(annot),names(annot))
f2[f2 %in% c("B","ILC3",'NK','pDC','Plasma','Tcyto','Th','Treg')] <- 'Lymphoid'
f2[f2 %in% c("Monocytes","Mast",'mDC','Macrophages')] <- 'Myeloid'
groups <- as.factor(c(setNames(paste(f1,'adr',sep=':'),names(f1)),setNames(paste(f2,'nb',sep=':'),names(f2))))
groups[grep('Erythroid|Myeloid|Lymph',groups)] <- NA;
groups[grep('Kidney|Cortex',groups)] <- NA;
groups <- droplevels(na.omit(groups))
table(groups)
groups
Chromaffin cells:adr Endothelial:adr Endothelial:nb Mesenchymal:adr Mesenchymal:nb Myofibroblasts:nb Noradrenergic:nb Pericytes:nb Schwann cells:adr SCP-like:nb
186 907 3584 969 2499 1873 15778 1863 47 987
Sympathoblasts:adr
163
#groups <- unlist(list(annot,adr.ann))
tcd <- cluster.expression.distances(conb,groups=groups,dist='JS',use.aggregated.matrices = T)
sort(tcd[grepl("SCP-like:nb",rownames(tcd)),grepl(":adr",colnames(tcd))],dec=T)
Endothelial:adr Chromaffin cells:adr Sympathoblasts:adr Mesenchymal:adr Schwann cells:adr
0.1520918 0.1472575 0.1331704 0.1212592 0.1180372
Using JS distance
cdist <- tcd[grepl(":nb",rownames(tcd)),grepl(":adr",colnames(tcd))]
#cdist <- tcd[levels(annot),levels(adr.ann)]
#cdist <- t(apply(cdist,1,function(x) (x-min(x))/max(x)))
rownames(cdist) <- gsub(":.*","",rownames(cdist))
colnames(cdist) <- gsub(":.*","",colnames(cdist))
col <- colorRampPalette(c('white','darkblue'))(1024)
#col <- circlize::colorRamp2(c(0.7, 0.9), c("white", "darkblue"))
Heatmap(1-cdist,col=col, cluster_rows = T,cluster_columns = T,border=T)

Correlation-based
tc <- conos:::rawMatricesWithCommonGenes(conb) %>%
lapply(conos:::collapseCellsByType, groups=as.factor(groups), min.cell.count=0) %>%
abind(along=3) %>%
apply(c(1,2),sum,na.rm=T)
lib.size.scale <- 1e6;
tc <- log10(t(tc/pmax(1,rowSums(tc)))*lib.size.scale+1)
tcd <- cor(tc)
Using top overdispersed genes
gns <- ap2$misc$varinfo %>% tibble::rownames_to_column('gene') %>% arrange(-qv) %>% head(2e3) %>% '$'('gene')
tcd <- cor(tc[rownames(tc) %in% gns,])
#tcd <- cor(tc)
sort(tcd[grepl("SCP-like:nb",rownames(tcd)),grepl(":adr",colnames(tcd))],dec=T)
Schwann cells:adr Sympathoblasts:adr Mesenchymal:adr Chromaffin cells:adr Endothelial:adr
0.6923011 0.6276368 0.6195420 0.6143442 0.4371182
cdist <- tcd[grepl(":nb",rownames(tcd)),grepl(":adr",colnames(tcd))]
#cdist <- tcd[levels(annot),levels(adr.ann)]
rownames(cdist) <- gsub(":.*","",rownames(cdist))
colnames(cdist) <- gsub(":.*","",colnames(cdist))
col <- colorRampPalette(c('white','darkblue'))(1024)
col <- circlize::colorRamp2(c(0.4, 0.7), c("white", "darkblue"))
cdist <- cdist[c(4,6,5,1,2,3),c(1,5,4,2,3)]
cm <- Heatmap(cdist,col=col, cluster_rows = F,cluster_columns = F,border=T,name='correlation',column_names_side='top',column_title = 'Adrenal cell types',row_title='NB cell types',column_title_side = 'bottom')
pdf(file='adrenal.match.pdf',width=4,height=3.9); print(cm); dev.off();
null device
1
cm

DEBUG
x <- na.omit(xa)
x <- na.omit(x[,names(cell.factor)])
x <- x>0
annot <- data.frame(clusters=cell.factor[colnames(x)],row.names = colnames(x))
ta <- ComplexHeatmap::HeatmapAnnotation(df=annot,border=T,show_legend = T)
ComplexHeatmap::Heatmap(x,col=colorRampPalette(c('white','gray20'))(1024),cluster_rows = F, cluster_columns = FALSE,show_row_names = T, show_column_names = F,top_annotation = ta,border=T)

source("~/m/p2/conos/R/plot.R")
sfac <- as.factor(fac2); sfac <- droplevels(sfac[unlist(tapply(1:length(fac),fac,sample,min(table(fac))))])
pp <- plotDEheatmap(conb,sfac,schwann.de2,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 7,column.metadata = list(tissue=tissuef))
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp

Try adrenal SCP markers
adr.de <- ap2$getDifferentialGenes(type='PCA',groups=adr.ann,append.auc=TRUE,z.threshold=0,upregulated.only=T)
source("~/m/p2/conos/R/plot.R")
pp <- plotDEheatmap(ap2,adr.ann,adr.de,n.genes.per.cluster = 10 ,show.gene.clusters=T,row.label.font.size = 6,order.clusters = T)
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
source("~/m/p2/conos/R/plot.R")
sfac <- adr.ann; sfac <- droplevels(sfac[sfac %in% c("Chromaffin cells","Schwann cells","Sympathoblasts")])
pp <- plotDEheatmap(ap2,sfac,adr.de,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 6)
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
Identify genes that distinguish the two populations
d.fac <- conb$getDatasetPerCell()
d.fac <- setNames(rep('other',length(d.fac)),names(d.fac))
d.fac[names(d.fac) %in% c(names(adr.ann)[adr.ann=='Schwann cells'])] <- 'normal SCPs'
d.fac[names(d.fac) %in% c(names(annot)[annot=='SCP-like'])] <- 'tumor SCPs'
tum.norm.de <- conb$getDifferentialGenes(groups=d.fac, n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
Combine in a heatmap:
Misc stuff
p1 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=annot,plot.na=F,mark.groups=T)
p2 <- ncdcon$plotGraph(alpha=0.03,size=0.5,gene='PRRX1',plot.na=F,mark.groups=T)
p3 <- ncdcon$plotGraph(alpha=0.03,size=0.5,gene='PHOX2B',plot.na=F,mark.groups=T)
plot_grid(plotlist=list(p1,p2,p3),nrow=1)

As factors
str(ascl1e)
Named num [1:81009] 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "names")= chr [1:81009] "NB26_AAACCTGAGATATGGT-1" "NB26_AAACCTGAGGATGTAT-1" "NB26_AAACCTGAGTCCATAC-1" "NB26_AAACCTGAGTGCGTGA-1" ...
p1 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=annot,plot.na=F,mark.groups=T)
p2 <- ncdcon$plotGraph(alpha=0.03,size=0.5,gene='SOX10',plot.na=F,mark.groups=T)
p3 <- ncdcon$plotGraph(alpha=1,size=2,groups=sox10e>0 & phox2be>0,plot.na=F,mark.groups=F,palette=c(adjustcolor('grey90',alpha=0.01),'red'))
plot_grid(plotlist=list(p1,p2,p3),nrow=1)

ncdcon$samples$NB26$plotEmbedding(groups=annot,type='PCA',mark.clusters = T,mark.cluster.cex = 1,embeddingType='tSNE')
using provided groups as a factor

names(dc)[dc]
[1] "NB26_GCTGGGTCAACTGCTA-1"
vc <- names(dc)[dc]; vc <- vc[grepl('^NB',vc)]
df <- data.frame(phox2b=phox2be[vc],prrx1=prrx1e[vc],type=annot[vc],cell=vc)
ggplot(df,aes(x=phox2b,y=prrx1,colour=type)) + geom_point()

plot(ncdcon$embedding[,1],ncdcon$embedding[,2],pch=19,cex=0.2,col=adjustcolor(1,alpha=0.01))
vc <- names(dc)[dc]
points(ncdcon$embedding[rownames(ncdcon$embedding) %in% vc,1],ncdcon$embedding[rownames(ncdcon$embedding) %in% vc,2],col=2)

emb <- acon$embedding
plot(emb[,1],emb[,2],pch=19,cex=0.2,col=adjustcolor(1,alpha=0.01))
vc <- names(dc)[dc]
table(vc %in% rownames(emb))
FALSE TRUE
23 12
points(emb[rownames(emb) %in% vc,1],emb[rownames(emb) %in% vc,2],col=2)

plot(ncdcon$embedding[,1],ncdcon$embedding[,2],pch=19,cex=0.2,col=sccore:::fac2col(annot[match(rownames(ncdcon$embedding),names(annot))]))
acon <- readRDS("~pkharchenko/m/ninib/NB/acon.rds")
#p1 <- acon$plotGraph(alpha=0.01,size=0.2,groups=con$getDatasetPerCell())
p1 <- acon$plotGraph(alpha=0.01,size=0.5,groups=scrubletf,mark.groups=F,plot.na=F,palette=function(n) c('gray','red'),title="Scrublet")
p2 <- acon$plotGraph(alpha=0.01,size=0.5,groups=bannot,plot.na=F,mark.groups=T,title="Annotations")
p3 <- acon$plotGraph(alpha=0.01,size=0.5,gene="MKI67",title="MKI67")
pp <- plot_grid(plotlist=list(p1,p3,p2),nrow=1)
pp
---
title: "R Notebook"
output: html_notebook
---

Load libraries

```{r echo=FALSE}
library(pagoda2)
library(dplyr)
library(conos)
library(parallel)
library(cowplot)
library(ggrepel)
library(Matrix)
#source("/home/pkharchenko/m/pavan/DLI/conp2.r")
```


Load kidney data
```{r}
source("~/m/pagoda2/R/helpers.r")
path <- "/d0-mendel/home/pkharchenko/ninib/NB/kidney/GSE114530"
pl <- paste(path,list.files(path=path),sep='/')
names(pl) <- list.files(path=path)
pl <- pl[!grepl('w9',names(pl))]
min.size <- 1e3;
cdl <- read.10x.matrices(pl,n.cores=30)
cdl <- lapply(cdl,function(d) d[,colSums(d)>=min.size,drop=F])

mdl <- lapply(pl,function(d) read.delim(paste(d,'annot.csv',sep='/'),sep=',',header=T,stringsAsFactors = F))
kidney.cell.f <- setNames(unlist(lapply(mdl,function(d) d[,2])),unlist(lapply(pagoda2:::sn(names(mdl)),function(n) paste(n,mdl[[n]][,1],sep='_'))))
```


Get cell cycle genes
```{r}
cc.genes <- readRDS("~pkharchenko/m/ninib/NB/cc.genes.rds")
```

Generate p2 apps, omitting cell cycle genes and re-calculating variance norm
```{r}
cdl.p2 <- lapply(cdl,function(cd) {
  cd <- cd[!rownames(cd)%in%cc.genes[,2], ]
  basicP2proc(cd,n.cores=30,make.geneknn = FALSE,get.tsne = T,get.largevis = FALSE)
})
```


```{r}
#cdl.p2 <- lapply(cdl,basicP2proc,n.cores=20,min.cells.per.gene=-1, get.largevis=F, get.tsne=T, make.geneknn=F,n.odgenes=2e3, nPcs=50)
```

Load adrenal data
This contains adrenal data
```{r}
ncdconA <- readRDS("~pkharchenko/m/ninib/NB/ncdconA.rds")
neww_annot <- readRDS("~pkharchenko/m/ninib/NB/neww_annot.rds")
```

```{r}
ap2 <- readRDS("~pkharchenko/m/ninib/NB/figures/fig1/ap2.p2.rds")
```

Recalculate without cell cycle genes and cluster 10
```{r}
x <- ap2$misc$rawCounts
f <- ap2$clusters$PCA[[1]];
x <- x[!rownames(x) %in% names(f)[f==10], !colnames(x) %in% cc.genes[,2]]
ap2b <- basicP2proc(t(x),n.cores=30,make.geneknn = FALSE,get.tsne = T,get.largevis = FALSE)
```



```{r}
cona <- Conos$new(c(cdl.p2,list("Adr"=ap2b)))
cona$buildGraph(k=15,k.self=5,k.self.weigh=0.1,ncomps=30,n.odgenes=2e3,space='PCA')
cona$findCommunities(method=leiden.community,resolution=1)
cona$embedGraph(method='UMAP',seed=1,spread=10)
cona$misc$embeddings <- list("UMAP"=cona$embedding)
```



```{r fig.height=8.5, fig.width=8.5}
size <- 0.1; alpha <- 0.5;
tissuef <- cona$getDatasetPerCell();
tissuef <- as.factor(setNames(ifelse(grepl('^w',names(tissuef)),'kidney','adrenal'),names(tissuef)))
pl <- list(cona$plotGraph(groups=tissuef, size=size, alpha=alpha,title='tissue',mark.groups=F),
           cona$plotGraph(groups=ap2$clusters$PCA[[1]],size=size, alpha=alpha,title='adrenal clusters',plot.na=F),
           cona$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,title='kidney',font.size=c(3,5),plot.na=F),
           cona$plotGraph(groups = neww_annot, size=size, alpha=alpha,title='adrenal',plot.na=F))
plot_grid(plotlist=pl,nrow=2)
```


```{r fig.height=4, fig.width=4}
cona$plotGraph(groups=setNames(ap2$clusters$PCA[[1]]==7,names(ap2$clusters$PCA[[1]])),size=size, alpha=alpha,title='adrenal clusters',plot.na=F)
```


```{r}
annot <- readRDS("/d0-mendel/home/meisl/Workplace/neuroblastoma/cell.annotation.Jan2020.rds")$cellano
annot <- setNames(as.character(annot),names(annot))
annot[annot=='unknown'] <- 'Erythroid'
annot[annot=='Plasmacytoid'] <- 'pDC'
annot[grep('Cytotoxic',annot)] <- 'Tcyto'
annot[grep('T helper',annot)] <- 'Th'
annot[grep('NK',annot)] <- 'NK'
annot[grep('Bcell',annot)] <- 'B'
annot[grep('PlasmaCell',annot)] <- 'Plasma'
annot[grep('Mast',annot)] <- 'Mast'
annot[grep("Bridge",annot)] <- 'SCP-like'
dannot <- as.factor(annot)

annot[grep("SOX11|Stress|euronal|Prolif",annot)] <- 'Noradrenergic'
annot[grep("Bridge",annot)] <- 'SCP-like'
annot <- as.factor(annot);
# renamings
levels(annot) <- gsub("Noradrenergic","Adrenergic",levels(annot))
```

Load annotations and palettes:
```{r}
load("../figures/fig1/annotations.RData")
```



Align everything together:

```{r}
conb <- Conos$new(c(cdl.p2,ncdconA$samples[grepl("^NB",names(ncdconA$samples))],list("Adr"=ap2b)))
```


```{r}
conb$buildGraph(k=15,k.self=20,k.self.weigh=0.5,ncomps=30,n.odgenes=2e3,space='PCA')
conb$findCommunities(method=leiden.community,resolution=1)
conb$embedGraph(method='UMAP',seed=0, min.dist=0.01, spread=10, min.prob.lower=1e-3,n.epochs=2e3)
conb$misc$embeddings <- list("UMAP"=conb$embedding)
```


```{r}
conb$misc$embeddings$largeVis <- conb$embedGraph(method='largeVis',alpha=3, sgd_batches = 3e8)
```

```{r}
conb$misc$embeddings$UMAP2 <- conb$embedGraph(method='UMAP', min.dist=0.01, spread=15, min.prob.lower=1e-3,n.epochs=2e3)
```



```{r fig.height=10, fig.width=10}
size <- 0.1; alpha <- 0.1;
tissuef <- conb$getDatasetPerCell();
tissuef <- setNames(as.character(tissuef),names(tissuef))
tissuef[grep("^w",tissuef)] <- 'kidney'
tissuef[grep("^Adr",tissuef)] <- 'adrenal'
tissuef[grep("^NB",tissuef)] <- 'NB'

pl <- list(conb$plotGraph(groups=tissuef, size=size, alpha=alpha,title='tissue',mark.groups=F),
           conb$plotGraph(groups=annot,size=size, alpha=alpha,title='neuroblastoma',plot.na=F),
           conb$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,title='kidney',font.size=c(3,5),plot.na=F),
          #conb$plotGraph(groups = neww_annot, size=size, alpha=alpha,title='adrenal',plot.na=F))
          conb$plotGraph(groups = ap2$clusters$PCA[[1]], size=size*4, alpha=alpha,title='adrenal',plot.na=F))

plot_grid(plotlist=pl,nrow=2)
```

```{r}
ninib_bannot <- readRDS("~pkharchenko/m/ninib/NB/ninib_bannot.rds")
```

Update adrenal annotation
```{r}
adr.ann <- setNames(as.character(neww_annot),names(neww_annot));
adr.ann[adr.ann=='Immune cells'] <- 'Kidney cells'
adr.ann[adr.ann=='Vessels'] <- 'Endothelial'
adr.ann[adr.ann=='Blood cells'] <- 'Erythroid'
adr.ann[adr.ann=='Pericytes'] <- 'Mesenchymal'
adr.ann[adr.ann=='Sympatoblasts'] <- 'Sympathoblasts'
adr.ann[adr.ann=='Schwann cells'] <- 'SCPs'
cl <- ap2$clusters$PCA[[1]]
adr.ann[names(adr.ann) %in% names(cl)[cl=='18']] <- "Myeloid"
adr.ann[names(adr.ann) %in% names(cl)[cl=='11']] <- "Lymphoid"
adr.ann[names(adr.ann) %in% names(cl)[cl=='14']] <- "Kidney cells"
adr.ann <- as.factor(adr.ann)

set.seed(4)
adr.pal <- sample(rainbow(length(levels(annot)),v=0.95,s=0.85));
adr.palf <- function(n) return(adr.pal)
```

With density contour
```{r}
linetype <- 1; color='grey20';
kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(annot)[annot=='Noradrenergic'],], compute.cont=TRUE)
cn <- with(kd, contourLines(x=eval.points[[1]], y=eval.points[[2]],z=estimate, levels=cont["10%"])[[1]])
cn <- geom_path(aes(x, y), data=data.frame(cn),linetype = linetype , color=color);

kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(annot)[annot=='Mesenchymal'],], compute.cont=TRUE)
cm <- with(kd, contourLines(x=eval.points[[1]], y=eval.points[[2]],z=estimate, levels=cont["10%"])[[1]])
cm <- geom_path(aes(x, y), data=data.frame(cm),linetype = linetype , color=color);

kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(annot)[annot=='SCP-like'],], compute.cont=TRUE, approx.cont = F)
#kd <- ks::kde(conb$embedding[rownames(conb$embedding) %in% names(ninib_bannot)[ninib_bannot=='Fibroblast_Bridge'],], compute.cont=TRUE)
cb <- with(kd, contourLines(x=eval.points[[1]], y=eval.points[[2]],z=estimate, levels=cont["50%"])[[1]])
cb <- geom_path(aes(x, y), data=data.frame(cb),linetype = linetype , color=color);

```




```{r fig.height=10, fig.width=10}
size <- 0.1; alpha <- 0.1;
tissuef <- conb$getDatasetPerCell();
tissuef <- setNames(as.character(tissuef),names(tissuef))
tissuef[grep("^w",tissuef)] <- 'kidney'
tissuef[grep("^Adr",tissuef)] <- 'adrenal'
tissuef[grep("^NB",tissuef)] <- 'NB'
raster <- T;

#af <- setNames(names(tissuef) %in% names(ninib_bannot)[ninib_bannot=='Fibroblast_Bridge'],names(tissuef))

pl <- list(conb$plotGraph(groups=tissuef, size=size, alpha=alpha,raster=raster, title='tissue',mark.groups=F)+ theme(legend.position=c(0.85, 0.15)) + guides(color=guide_legend(override.aes = list(size=3,alpha=0.8),title='Tissue:')),
           conb$plotGraph(groups=annot,size=size, alpha=alpha,raster=raster,title='neuroblastoma',plot.na=F,palette=annot.pal),
           conb$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,raster=raster,title='kidney',font.size=c(3,5),plot.na=F),
          conb$plotGraph(groups = adr.ann, size=size*4, alpha=alpha*2,raster=raster,title='adrenal',plot.na=F,palette=adr.palf,font.size=c(4,5)))
          #conb$plotGraph(groups = ap2$clusters$PCA[[1]], size=size*4, alpha=alpha*2,title='adrenal',plot.na=F))

pl <- lapply(pl,function(p) p+cn+cm+cb+ xlim(range(conb$embedding[,1]))+ylim(range(conb$embedding[,2])))

pp <- plot_grid(plotlist=pl,nrow=2)

pp
```

```{r}
pdf('combined.pdf',width=10,heigh=10); print(pp); dev.off();
invisible(lapply(1:length(pl),function(i) { pdf(paste('panel',i,'pdf',sep='.'),width=4,height=4); print(pl[[i]]); dev.off()}))
```


Restricted window plot for the main figure:

```{r fig.height=6, fig.width=3.5}
size <- 0.3; alpha <- 0.1;
raster <- T;
xlim <- c(-66,40); ylim <- c(-63,0)

#af <- setNames(names(tissuef) %in% names(ninib_bannot)[ninib_bannot=='Fibroblast_Bridge'],names(tissuef))

pl <- list(conb$plotGraph(groups=tissuef, size=size, alpha=alpha,raster=raster, raster.width=6.6,raster.height=4, title='tissue',mark.groups=F),
           conb$plotGraph(groups=annot,size=size, alpha=alpha,raster=raster,title='neuroblastoma', raster.width=6.6,raster.height=4,plot.na=F,palette=annot.pal),
           #conb$plotGraph(groups = kidney.cell.f, size=size, alpha=alpha,raster=raster,title='kidney',font.size=c(3,5),plot.na=F),
          conb$plotGraph(groups = adr.ann, size=size*4, alpha=alpha*2,raster=raster,title='adrenal', raster.width=6.6,raster.height=4,plot.na=F,palette=adr.palf,font.size=c(4,5)))
          #conb$plotGraph(groups = ap2$clusters$PCA[[1]], size=size*4, alpha=alpha*2,title='adrenal',plot.na=F))

#pl <- lapply(pl,function(p) p+cn+cm+cb+ coord_cartesian(xlim,ylim,clip="on"))
# pl <- lapply(pl,function(p) p+cn+cm+cb+scale_x_continuous(expand = c(0, 0)) +
#   scale_y_continuous(expand = c(0, 0)) + xlim(xlim)+ylim(ylim)
#   )

pl <- lapply(pl,function(p) p+cn+cm+cb+coord_cartesian(expand = c(0, 0), xlim=xlim,ylim=ylim) +xlim(xlim)+ylim(ylim))


pp <- plot_grid(plotlist=pl,ncol=1)
pdf('combined_narrow.pdf',width=3.5,heigh=6); print(pp); dev.off();
invisible(lapply(1:length(pl),function(i) { pdf(paste('panel',i,'narrow','pdf',sep='.'),width=3.5,height=2); print(pl[[i]]); dev.off()}))
pp
```


```{r fig.height=4,fig.width=4}
conb$plotGraph(gene='NR5A1',alpha=0.4,size=0.1)
```

```{r}
jgrp <- conb$findCommunities(method=leiden.community,r=4)$groups
```

```{r fig.height=6,fig.width=6}
conb$plotGraph(alpha=0.1,size=0.1,mark.groups=T,groups=jgrp)
```


Illustrate similarity between bridge and SCPs:

Identify genes that set both populations apart from others:
```{r}
fac <- conb$getDatasetPerCell()
fac <- setNames(rep('other',length(fac)),names(fac))
#fac[names(fac) %in% c(names(adr.ann)[adr.ann=='Schwann cells'], names(annot)[annot=='SCP-like'])] <- 'SCPs'
schwann.de <- conb$getDifferentialGenes(groups=fac,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
```

```{r fig.width=5,fig.height=5}
source("~/m/p2/conos/R/plot.R")
sfac <- fac; sfac <- sfac[unlist(tapply(1:length(fac),fac,sample,min(table(fac))))]
pp <- plotDEheatmap(conb,sfac,schwann.de,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 6,column.metadata = list(tissue=tissuef))
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
```

```{r}
fac2 <- conb$getDatasetPerCell()
fac2 <- setNames(rep('other',length(fac2)),names(fac2))
#fac[names(fac) %in% c(names(adr.ann)[adr.ann=='Schwann cells'], names(annot)[annot=='SCP-like'])] <- 'SCPs'
fac2[names(fac2) %in% c(names(jgrp)[jgrp=='37'])] <- 'SCPs'
schwann.de2 <- conb$getDifferentialGenes(groups=fac2,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
```

```{r fig.width=14,fig.height=7}
source("~/m/p2/conos/R/plot.R")
sfac <- as.factor(fac2); sfac <- droplevels(sfac[unlist(tapply(1:length(fac),fac,sample,min(table(fac))))])
pp <- plotDEheatmap(conb,sfac,schwann.de2,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 7,column.metadata = list(tissue=tissuef))
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
```

There's a group within the 'other' that seems to share the SCP pattern ...
```{r fig.height=4,fig.width=4}
conb$plotGraph(gene='PLP1',alpha=0.4,size=0.1)
```

```{r fig.height=4,fig.width=4}
conb$plotGraph(gene='S100B',alpha=0.4,size=0.1)
```

```{r}
qplot <- function(g, con.obj, ann) {
  #cat(g,' ')
  x <- lapply(con.obj$samples[5:10],function(r) { if(g%in% colnames(r$counts)) { r$counts[,g] } else { return(NULL) } })
  if(length(unlist(x))<1) stop('gene ',g,' is not found')
  df <- data.frame(val=unlist(x),cell=unlist(lapply(x,names)))
  df$cluster <- ann[match(df$cell,names(ann))]
  df <- na.omit(df)
  
  mv <- max(tapply(df$val,df$cluster,quantile,p=0.8),tapply(df$val,df$cluster,mean))*1.5
  p <- ggplot(df,aes(x=cluster,y=val,color=cluster))+geom_boxplot(outlier.shape = NA)+ stat_summary(fun.data=mean_se,geom="pointrange", color="black")+ylab(g)+ggtitle(g)+guides(colour=FALSE)+ theme(axis.text.x = element_text(angle = 90, hjust = 1,vjust=0.5))+coord_cartesian(ylim=c(0,mv));
  p
}
```


```{r}
markers <- c(as.character(schwann.de2$SCPs %>% arrange(-AUC) %>% head(8) %>% '$'('Gene')),'SOX10','MIA')
```

```{r fig.width=14,fig.height=20}
pl <- lapply(markers,qplot,conb,jgrp)
plot_grid(plotlist=pl,ncol=2)
```

It's cluster 35, which is the extension of the bridge into the fibroblasts ... most likely as a result of clustering uncertainty. 
The cluster should be better defined on NB alone ...

Check expression on the NB embedding
```{r}
ncdcon <- readRDS("~pkharchenko/m/ninib/NB/ncdcon.rds")
```


```{r fig.width=15,fig.height=5}
p1 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=ncdcon$clusters$rleiden$groups,plot.na=F,mark.groups=T)
p2 <- ncdcon$plotGraph(alpha=0.03,size=0.5,plot.na=F,mark.groups=T,groups=fac)
p3 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=fac2,plot.na=F,mark.groups=T)
plot_grid(plotlist=list(p1,p2,p3),nrow=1)
```


```{r fig.width=14,fig.height=20}
pl <- lapply(markers,qplot,ncdcon,ncdcon$clusters$rleiden$groups)
plot_grid(plotlist=pl,ncol=2)
```

Here to, while cluster 1-5 captures most of the SCPs, part of the signature is still notable in the cluster 5-1 that connects to the messenchymal tumor cells


Also, in doing DE on fac2, the adrenal and NB contributions to the SCP population are very unbalanced. It's probably best to run the two separately and intersect the high-AUC genes:

```{r}
# factor for just the adrenal SCP cells
fac3.adr <- conb$getDatasetPerCell()
fac3.adr <- setNames(rep('other',length(fac3.adr)),names(fac3.adr))
# remove NB SCP-like cells
fac3.adr <- fac3.adr[!names(fac3.adr) %in% c(names(jgrp)[jgrp=='35'], names(annot)[annot=='SCP-like'], intersect(names(annot),names(jgrp)[jgrp=='37']))]
fac3.adr[names(fac3.adr) %in% c(names(adr.ann)[adr.ann=='Schwann cells'])] <- 'adrenal SCP'
#fac3.adr[names(fac3.adr) %in% c(names(jgrp)[jgrp=='37'])] <- 'SCPs' # use a more restrictive cluster-based definition
fac3.adr <- droplevels(as.factor(fac3.adr))
table(fac3.adr,conb$getDatasetPerCell()[names(fac3.adr)])
schwann.de3.adr <- conb$getDifferentialGenes(groups=fac3.adr,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
```

And NB only cells
```{r}
# factor for just the adrenal SCP cells
fac3.nb <- conb$getDatasetPerCell()
fac3.nb <- setNames(rep('other',length(fac3.nb)),names(fac3.nb))
# remove NB SCP-like cells
fac3.nb <- fac3.nb[!names(fac3.nb) %in% c(names(jgrp)[jgrp=='35'], names(adr.ann)[adr.ann=='Schwann cells'])]
fac3.nb[names(fac3.nb) %in% c(intersect(names(annot),names(jgrp)[jgrp=='37']))] <- 'SCP-like NB'
fac3.nb <- droplevels(as.factor(fac3.nb))
table(fac3.nb,conb$getDatasetPerCell()[names(fac3.nb)])
schwann.de3.nb <- conb$getDifferentialGenes(groups=fac3.nb,n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
```

Join tables and separate common and distinct markers
```{r}
require(magrittr)
df <- full_join(schwann.de3.adr[['adrenal SCP']], schwann.de3.nb[['SCP-like NB']], by='Gene',suffix=c('.adr','.nb'))
df$minAUC <- pmin(df$AUC.adr,df$AUC.nb)
df$maxAUC <- pmax(df$AUC.adr,df$AUC.nb,na.rm=T)
df %<>% arrange(-minAUC,maxAUC) %>% select(Gene,minAUC,maxAUC,AUC.nb,AUC.adr)
```

```{r}
head(df,100)
```

```{r}
tail(df,100)
```

Show combined expression pattern
```{r}
genes.nb <- tail(df[is.na(df$AUC.adr),],7)$Gene #7

genes.adr <- tail(df[is.na(df$AUC.nb),],6)$Gene
genes.adr <- unique(c(genes.adr,'ASCL1'))
#genes.common <- c(head(df,14)$Gene,'SOX10','ASCL1','EGR2','DHH','OCT4','ISL1') #14
genes.common <- unique(c(head(df,13)$Gene,'SOX10',"FOXD3" , "PMP2", "LGI4", "SEMA3B")) #14

genes <- unique(c(genes.common,genes.adr,genes.nb))

#cell.factor <- as.factor(c(setNames(rep('NB SCP-like',length(c1)),c1),setNames(rep("adrenal SCP",length(c2)),c2)))
cell.factor <- c(setNames(as.character(fac3.nb),names(fac3.nb)),setNames(as.character(fac3.adr),names(fac3.adr)))
cell.factor <- cell.factor[unique(names(cell.factor))]
oc <- names(cell.factor)[cell.factor=='other']; cell.factor[oc] <- paste('other',as.character(tissuef[oc]),sep=' ')

cell.factor <- factor(cell.factor,levels=unique(cell.factor)[c(5,3,2,4,1)])

# omit kidney
cell.factor[cell.factor=='other kidney'] <- NA; cell.factor <- droplevels(na.omit(cell.factor))

xa <- do.call(rbind,lapply(pagoda2:::sn(genes),function(gene) conos:::getGeneExpression(conb,gene)))
```

Because the numbers of cells are so unbalanced, we'll randomly collapse larger groups to approximately match the number of cells in the smaller groups
```{r}
xc <- do.call(cbind, tapply(1:ncol(xa),cell.factor[colnames(xa)],function(ii) {
  bin.size <- ceiling(length(ii)/min(table(cell.factor)))
  nbins <- ceiling(length(ii)/bin.size)
  if(bin.size >1 ) {
    bf <- sample(rep(1:nbins,bin.size))[1:length(ii)]
    #x <- do.call(cbind,tapply(1:length(ii),as.factor(bf),function(x) rowMeans(xa[,ii[x]])))
    x <- do.call(cbind,tapply(1:length(ii),as.factor(bf),function(x) xa[,sample(ii[x],1),drop=F]))
    #colnames(x) <- colnames(xa)[ii][1:ncol(x)]
    x
  } else {
    return(xa[,ii])
  }
}))
```


```{r}
# trim
expression.quantile <- 0.75
x <- na.omit(xc)
#x <- na.omit(x[,names(cell.factor)])
x <- t(apply(x, 1, function(xp) {
  
  if(expression.quantile<1) {
      qs <- quantile(xp,c(1-expression.quantile,expression.quantile))
      if(diff(qs)==0) { # too much, set to adjacent values
        xps <- unique(xp)
        if(length(xps)<3) { qs <- range(xp) } # only two values, just take the extremes
        xpm <- median(xp)
        if(sum(xp<xpm) > sum(xp>xpm)) { # more common to have values below the median
          qs[1] <- max(xp[xp<xpm])
        } else { # more common to have values above the median
          qs[2] <- min(xps[xps>xpm]) # take the next one higher
        }
      }
      xp[xp<qs[1]] <- qs[1]
      xp[xp>qs[2]] <- qs[2]
    }
    xp <- xp-min(xp);
    if(max(xp)>0) xp <- xp/max(xp);
    xp
    
  # qs <- quantile(xp,c(1-expression.quantile,expression.quantile))
  # #xp[xp<qs[1]] <- qs[1]
  # if(qs[2]>0) {
  #   xp[xp>qs[2]] <- qs[2]
  # }
  # xp-min(xp);
  # xpr <- diff(range(xp));
  # if(xpr>0) xp <- xp/xpr;
  # xp
}))


```

```{r fig.width=4,fig.height=7}
require(ComplexHeatmap)
# column annotation
an <- data.frame(cells=cell.factor[colnames(x)],row.names = colnames(x))
# row annotation
gpal <- c("common SCP genes"='grey30','adrenal'='green','NB'='orange');
rdf <- data.frame(genes=rep(names(gpal),c(length(genes.common),length(genes.adr),length(genes.nb))),name=c(genes.common,genes.adr,genes.nb)); 
rdf <- rdf[!duplicated(rdf$name),];
rdf <- data.frame(genes=rdf$genes,row.names=rdf$name)
#rownames(rdf) <- c(genes.common,genes.adr,genes.nb); 
rdf <- rdf[rownames(x),,drop=F]
ra <- ComplexHeatmap::HeatmapAnnotation(df=rdf,which='row',show_annotation_name=FALSE, show_legend=FALSE, border=T,col=list(genes=gpal))

ra <- ComplexHeatmap::HeatmapAnnotation(genes=anno_block(gp=gpar(fill=NA,col=NA),labels=names(gpal[levels(rdf$genes)]),labels_gp = gpar(fontsize=16) ),which='row',show_annotation_name=FALSE, show_legend=FALSE, border=T)

#ta <- ComplexHeatmap::HeatmapAnnotation(df=an,border=T,show_legend = T,col=list(cells=c('adrenal SCP'='darkorchid4','SCP-like NB'='red','other NB'='orange','other adrenal'='green','other kidney'='royalblue3')))

gpal <- c('adrenal SCP'='darkorchid4','SCP-like NB'='red','other NB'='orange','other adrenal'='green');
ta <- ComplexHeatmap::HeatmapAnnotation(cells=anno_block(gp=gpar(fill=NA,col=NA), labels=names(gpal), labels_rot = 35,  labels_gp = gpar(just=-1, fontsize=14)))

#ComplexHeatmap::Heatmap(x,name='Expression',col=colorRampPalette(c('dodgerblue1','grey95','indianred1'))(1024), left_annotation=ra, cluster_rows = F, cluster_columns = FALSE,show_row_names = T, show_column_names = F,top_annotation = ta,border=T)
set.seed(0)
hm <- ComplexHeatmap::Heatmap(x,name='Expression',col=colorRampPalette(c('dodgerblue1','grey95','indianred1'))(1024), left_annotation=ra, cluster_rows = F, cluster_columns = FALSE,show_row_names = T, show_column_names = F,top_annotation = ta,border=T,column_split=an[colnames(x),1], row_split=rdf[,1], row_gap = unit(1, "mm"), column_gap = unit(1, "mm"),row_names_gp = grid::gpar(fontsize = 8),column_title = NULL, row_title = NULL, heatmap_legend_param = list(legend_direction='horizontal'),use_raster = T,raster_device = "CairoPNG")
hmp <- draw(hm, heatmap_legend_side = "bottom")
pdf(file='scp.markers2.pdf',width=4,height=7); print(hmp); dev.off();
```



### Cell type similarity matrix

Let's go with the simplest thing
```{r}
source("~pkharchenko/m/pavan/DLI/conp2.r")
f1 <- setNames(as.character(adr.ann),names(adr.ann))
#f1[f1 %in% c("Monocytes","Mast",'mDC')] <- 'Myeloid'
f2 <- setNames(as.character(annot),names(annot))
f2[f2 %in% c("B","ILC3",'NK','pDC','Plasma','Tcyto','Th','Treg')] <- 'Lymphoid'
f2[f2 %in% c("Monocytes","Mast",'mDC','Macrophages')] <- 'Myeloid'

groups <- as.factor(c(setNames(paste(f1,'adr',sep=':'),names(f1)),setNames(paste(f2,'nb',sep=':'),names(f2))))
groups[grep('Erythroid|Myeloid|Lymph',groups)] <- NA; 
groups[grep('Kidney|Cortex',groups)] <- NA; 
groups <- droplevels(na.omit(groups))
table(groups)                                        
#groups <- unlist(list(annot,adr.ann))
tcd <- cluster.expression.distances(conb,groups=groups,dist='JS',use.aggregated.matrices = T)
sort(tcd[grepl("SCP-like:nb",rownames(tcd)),grepl(":adr",colnames(tcd))],dec=T)
```


Using JS distance
```{r fig.width=4.5,fig.height=5}
cdist <- tcd[grepl(":nb",rownames(tcd)),grepl(":adr",colnames(tcd))]
#cdist <- tcd[levels(annot),levels(adr.ann)]
#cdist <- t(apply(cdist,1,function(x) (x-min(x))/max(x)))
rownames(cdist) <- gsub(":.*","",rownames(cdist))
colnames(cdist) <- gsub(":.*","",colnames(cdist))
col <- colorRampPalette(c('white','darkblue'))(1024)
#col <- circlize::colorRamp2(c(0.7, 0.9), c("white", "darkblue"))
Heatmap(1-cdist,col=col, cluster_rows = T,cluster_columns = T,border=T)
```

Correlation-based
```{r}
tc <- conos:::rawMatricesWithCommonGenes(conb) %>% 
  lapply(conos:::collapseCellsByType, groups=as.factor(groups), min.cell.count=0) %>%
  abind(along=3) %>%
  apply(c(1,2),sum,na.rm=T)
lib.size.scale <- 1e6;
tc <- log10(t(tc/pmax(1,rowSums(tc)))*lib.size.scale+1)

#tcd <- cor(tc)

```

Using top overdispersed genes
```{r}
gns <- ap2$misc$varinfo %>% tibble::rownames_to_column('gene') %>% arrange(-qv) %>% head(2e3) %>% '$'('gene')
tcd <- cor(tc[rownames(tc) %in% gns,])
#tcd <- cor(tc)
sort(tcd[grepl("SCP-like:nb",rownames(tcd)),grepl(":adr",colnames(tcd))],dec=T)
```


```{r fig.width=4.5,fig.height=4.2}
cdist <- tcd[grepl(":nb",rownames(tcd)),grepl(":adr",colnames(tcd))]
#cdist <- tcd[levels(annot),levels(adr.ann)]
rownames(cdist) <- gsub(":.*","",rownames(cdist))
colnames(cdist) <- gsub(":.*","",colnames(cdist))
col <- colorRampPalette(c('white','darkblue'))(1024)
col <- circlize::colorRamp2(c(0.4, 0.7), c("white", "darkblue"))
cdist <- cdist[c(4,6,5,1,2,3),c(1,5,4,2,3)]
cm <- Heatmap(cdist,col=col, cluster_rows = F,cluster_columns = F,border=T,name='correlation',column_names_side='top',column_title = 'Adrenal cell types',row_title='NB cell types',column_title_side = 'bottom')
pdf(file='adrenal.match.pdf',width=4,height=3.9); print(cm); dev.off();
cm
```










# DEBUG

```{r}
x <- na.omit(xa)
x <- na.omit(x[,names(cell.factor)])
x <- x>0
class(x) <- 'numeric'
```


```{r fig.width=6,fig.height=10}
annot <- data.frame(clusters=cell.factor[colnames(x)],row.names = colnames(x))
ta <- ComplexHeatmap::HeatmapAnnotation(df=annot,border=T,show_legend = T)
ComplexHeatmap::Heatmap(x,col=colorRampPalette(c('white','gray20'))(1024),cluster_rows = F, cluster_columns = FALSE,show_row_names = T, show_column_names = F,top_annotation = ta,border=T)
```


```{r fig.width=14,fig.height=7}
source("~/m/p2/conos/R/plot.R")
sfac <- as.factor(fac2); sfac <- droplevels(sfac[unlist(tapply(1:length(fac),fac,sample,min(table(fac))))])
pp <- plotDEheatmap(conb,sfac,schwann.de2,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 7,column.metadata = list(tissue=tissuef))
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
```



Try adrenal SCP markers
```{r}
adr.de <- ap2$getDifferentialGenes(type='PCA',groups=adr.ann,append.auc=TRUE,z.threshold=0,upregulated.only=T)
```

```{r fig.width=6,fig.height=8}
source("~/m/p2/conos/R/plot.R")
pp <- plotDEheatmap(ap2,adr.ann,adr.de,n.genes.per.cluster = 10 ,show.gene.clusters=T,row.label.font.size = 6,order.clusters = T)
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
```


```{r fig.width=6,fig.height=8}
source("~/m/p2/conos/R/plot.R")
sfac <- adr.ann; sfac <- droplevels(sfac[sfac %in% c("Chromaffin cells","Schwann cells","Sympathoblasts")])
pp <- plotDEheatmap(ap2,sfac,adr.de,n.genes.per.cluster = 30 ,show.gene.clusters=T,row.label.font.size = 6)
#pdf(file='adr.heatmap.pdf',width=7,height=15); print(pp); dev.off();
pp
```

```{r}

```

Identify genes that distinguish the two populations
```{r}
d.fac <- conb$getDatasetPerCell()
d.fac <- setNames(rep('other',length(d.fac)),names(d.fac))
d.fac[names(d.fac) %in% c(names(adr.ann)[adr.ann=='Schwann cells'])] <- 'normal SCPs'
d.fac[names(d.fac) %in% c(names(annot)[annot=='SCP-like'])] <- 'tumor SCPs'
tum.norm.de <- conb$getDifferentialGenes(groups=d.fac, n.cores=30,append.auc=TRUE,z.threshold=0,upregulated.only=T)
```

Combine in a heatmap:



### Misc stuff
```{r fig.width=15,fig.height=5}

p1 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=annot,plot.na=F,mark.groups=T)
p2 <- ncdcon$plotGraph(alpha=0.03,size=0.5,gene='PRRX1',plot.na=F,mark.groups=T)
p3 <- ncdcon$plotGraph(alpha=0.03,size=0.5,gene='PHOX2B',plot.na=F,mark.groups=T)
plot_grid(plotlist=list(p1,p2,p3),nrow=1)
```
As factors
```{r}
ascl1e <- conos:::getGeneExpression(ncdcon,'ASCL1')
phox2be <- conos:::getGeneExpression(ncdcon,'PHOX2B')
prrx2e <- conos:::getGeneExpression(ncdcon,'PRRX2')
prrx1e <- conos:::getGeneExpression(ncdcon,'PRRX1')
sox10e <- conos:::getGeneExpression(ncdcon,'SOX10')
foxd3e <- conos:::getGeneExpression(ncdcon,'FOXD3')

dc <- sox10e>0 & phox2be>0
x <- lapply(ncdcon$samples,function(d) d[['misc']][['rawCounts']])
```

```{r fig.width=15,fig.height=5}
p1 <- ncdcon$plotGraph(alpha=0.03,size=0.5,groups=annot,plot.na=F,mark.groups=T)
p2 <- ncdcon$plotGraph(alpha=0.03,size=0.5,gene='SOX10',plot.na=F,mark.groups=T)
p3 <- ncdcon$plotGraph(alpha=1,size=2,groups=sox10e>0 & phox2be>0,plot.na=F,mark.groups=F,palette=c(adjustcolor('grey90',alpha=0.01),'red'))
plot_grid(plotlist=list(p1,p2,p3),nrow=1)
```



```{r fig.width=5, fig.height=5}
ncdcon$samples$NB26$plotEmbedding(groups=annot,type='PCA',mark.clusters = T,mark.cluster.cex = 1,embeddingType='tSNE')
```

```{r fig.width=5, fig.height=5}
#dc <- sox10e>0 & ascl1e>0
dc <- sox10e>0 & phox2be>0
dc <- foxd3e>0 & phox2be>0
dc <- sox10e>0 & prrx2e>0
dc <- sox10e>0 & prrx1e>0
dc <- phox2be>0 & prrx1e>0
dc <- phox2be>0.3 & prrx1e>0.3 & sox10e>0
table(dc)
names(dc)[dc]
em <- ncdcon$samples$NB26$embeddings$PCA[[2]]
plot(em[,1],em[,2],pch=19,cex=0.2,col=adjustcolor(1,alpha=0.01))
vc <- names(dc)[dc]
points(em[rownames(em) %in% vc,1],em[rownames(em) %in% vc,2],col=2)
```


```{r fig.width=5, fig.height=5}
vc <- names(dc)[dc]; vc <- vc[grepl('^NB',vc)]
df <- data.frame(phox2b=phox2be[vc],prrx1=prrx1e[vc],type=annot[vc],cell=vc)
ggplot(df,aes(x=phox2b,y=prrx1,colour=type)) + geom_point()
```


```{r fig.width=5, fig.height=5}
plot(ncdcon$embedding[,1],ncdcon$embedding[,2],pch=19,cex=0.2,col=adjustcolor(1,alpha=0.01))
vc <- names(dc)[dc]
points(ncdcon$embedding[rownames(ncdcon$embedding) %in% vc,1],ncdcon$embedding[rownames(ncdcon$embedding) %in% vc,2],col=2)
```

```{r fig.width=5, fig.height=5}
emb <- acon$embedding
plot(emb[,1],emb[,2],pch=19,cex=0.2,col=adjustcolor(1,alpha=0.01))
vc <- names(dc)[dc]
table(vc %in% rownames(emb))
points(emb[rownames(emb) %in% vc,1],emb[rownames(emb) %in% vc,2],col=2)
```

```{r fig.width=5, fig.height=5}
plot(ncdcon$embedding[,1],ncdcon$embedding[,2],pch=19,cex=0.2,col=sccore:::fac2col(annot[match(rownames(ncdcon$embedding),names(annot))]))
```

```{r}
acon <- readRDS("~pkharchenko/m/ninib/NB/acon.rds")
```

```{r fig.width=10,fig.height=3.5}
#p1 <- acon$plotGraph(alpha=0.01,size=0.2,groups=con$getDatasetPerCell())
p1 <- acon$plotGraph(alpha=0.01,size=0.5,groups=scrubletf,mark.groups=F,plot.na=F,palette=function(n) c('gray','red'),title="Scrublet")
p2 <- acon$plotGraph(alpha=0.01,size=0.5,groups=bannot,plot.na=F,mark.groups=T,title="Annotations")
p3 <- acon$plotGraph(alpha=0.01,size=0.5,gene="MKI67",title="MKI67")
pp <- plot_grid(plotlist=list(p1,p3,p2),nrow=1)
pp
```